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Future Issues with Credit Card Fraud Detection Techniques

Future Issues with Credit Card Fraud Detection Techniques
Author: Marvin Namanda
Publisher: GRIN Verlag
Total Pages: 15
Release: 2016-05-20
Genre: Business & Economics
ISBN: 3668222584

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Research Paper (undergraduate) from the year 2016 in the subject Business economics - Information Management, grade: 1, Federation University Australia, course: ITECH1006, language: English, abstract: Fraud is a contemporary ethical issue whose complexity is growing by day. The aims of this study are to identify the types of credit card fraud and to stipulate the future issues with the sector. The minor aim is to compare and analyze recent publication findings in future issues with credit card fraud detection. The significance of this paper is to allow the appreciation of the future issues with respect to credit card fraud detection techniques.


Detecting Credit Card Fraud

Detecting Credit Card Fraud
Author:
Publisher:
Total Pages: 70
Release: 2020
Genre:
ISBN:

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Advancements in the modern age have brought many conveniences, one of those being credit cards. Providing an individual the ability to hold their entire purchasing power in the form of pocket-sized plastic cards have made credit cards the preferred method to complete financial transactions. However, these systems are not infallible and may provide criminals and other bad actors the opportunity to abuse them. Financial institutions and their customers lose billions of dollars every year to credit card fraud. To combat this issue, fraud detection systems are deployed to discover fraudulent activity after they have occurred. Such systems rely on advanced machine learning techniques and other supportive algorithms to detect and prevent fraud in the future. This work analyzes the various machine learning techniques for their ability to efficiently detect fraud and explores additional state-of-the-art techniques to assist with their performance. This work also proposes a generalized strategy to detect fraud regardless of a dataset's features or unique characteristics. The high performing models discovered through this generalized strategy lay the foundation to build additional models based on state-of-the-art methods. This work expands on the issues of fraud detection, such as missing data and unbalanced datasets, and highlights models that combat these issues. Furthermore, state-of-the-art techniques, such as adapting to concept drift, are employed to combat fraud adaptation.


Fraud Prevention Techniques for Credit Card Fraud

Fraud Prevention Techniques for Credit Card Fraud
Author: David A. Montague
Publisher: Trafford Publishing
Total Pages: 218
Release: 2004
Genre: Business & Economics
ISBN: 1412014603

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Fraud is nothing new to the merchant. Since the beginning of time, man has always looked for the opportunity to defraud others - to gain goods or services without making payment. For the credit card industry, fraud is a part of doing business, and is something that is always a challenge. The merchants that are the best at preventing fraud are the ones that can adapt to change quickly. This book is written to provide information about how to prevent credit card fraud in the card-not-present space (mail order, telephone order, e-commerce). This book is meant to be an introduction to combating fraud, providing the basic concepts around credit card payment, the ways fraud is perpetrated, along with write ups that define and provide best practices on the use of 32 fraud-prevention techniques. 32 Detailed Fraud Prevention Techniques How to catch the Chameleon on the web Top 10 rules to prevent credit card fraud Understand common fraud schemes The one Fraud Prevention Technique no merchant can afford not to do Details on over 40 Vendors that sell fraud prevention tools and services, along with how to build it in-house Learn the anatomy of a Fraud Prevention Strategy


Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques

Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques
Author: Bart Baesens
Publisher: John Wiley & Sons
Total Pages: 402
Release: 2015-07-27
Genre: Computers
ISBN: 1119146828

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Detect fraud earlier to mitigate loss and prevent cascading damage Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, model building, and post-implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. These techniques are effective for fraud detection across industry boundaries, including applications in insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, giving you a highly practical framework for fraud prevention. It is estimated that a typical organization loses about 5% of its revenue to fraud every year. More effective fraud detection is possible, and this book describes the various analytical techniques your organization must implement to put a stop to the revenue leak. Examine fraud patterns in historical data Utilize labeled, unlabeled, and networked data Detect fraud before the damage cascades Reduce losses, increase recovery, and tighten security The longer fraud is allowed to go on, the more harm it causes. It expands exponentially, sending ripples of damage throughout the organization, and becomes more and more complex to track, stop, and reverse. Fraud prevention relies on early and effective fraud detection, enabled by the techniques discussed here. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.


Anomaly Detection in Credit Card Transactions Using Machine Learning

Anomaly Detection in Credit Card Transactions Using Machine Learning
Author: Meenu
Publisher:
Total Pages: 5
Release: 2020
Genre:
ISBN:

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Anomaly Detection is a method of identifying the suspicious occurrence of events and data items that could create problems for the concerned authorities. Data anomalies are usually associated with issues such as security issues, server crashes, bank fraud, building structural flaws, clinical defects, and many more. Credit card fraud has now become a massive and significant problem in today's climate of digital money. These transactions carried out with such elegance as to be similar to the legitimate one. So, this research paper aims to develop an automatic, highly efficient classifier for fraud detection that can identify fraudulent transactions on credit cards. Researchers have suggested many fraud detection methods and models, the use of different algorithms to identify fraud patterns. In this study, we review the Isolation forest, which is a machine learning technique to train the system with the help of H2O.ai. The Isolation Forest was not so much used and explored in the area of anomaly detection. The overall performance of the version evaluated primarily based on widely-accepted metrics: precision and recall. The test data used in our research come from Kaggle.


Machine Learning Advances in Payment Card Fraud Detection

Machine Learning Advances in Payment Card Fraud Detection
Author: Nick Ryman-Tubb
Publisher: Academic Press
Total Pages: 350
Release: 2019-06-15
Genre: Business & Economics
ISBN: 9780128134153

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Machine Learning Advances in Payment Card Fraud Detection provides a thorough review of the state-of-the-art in fraud detection research that is ideal for graduate level readers and professionals. Through a comprehensive examination of fraud analytics that covers data collection, steps for cleaning and processing data, tools for analyzing data, and ways to draw insights, the book introduces state-of the-art payment fraud detection techniques. Other topics covered include machine learning techniques for the detection of fraud, including SOAR, and opportunities for future research, such as developing holistic approaches for countering fraud. Covers analytical approaches and machine learning for fraud detection Explores SOAR with full R-code and example obfuscated datasets in a freely-accessible companion website Introduces state-of the-art payment fraud detection techniques


Soft Computing for Intelligent Systems

Soft Computing for Intelligent Systems
Author: Nikhil Marriwala
Publisher: Springer Nature
Total Pages: 653
Release: 2021-06-22
Genre: Technology & Engineering
ISBN: 9811610487

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This book presents high-quality research papers presented at the International Conference on Soft Computing for Intelligent Systems (SCIS 2020), held during 18–20 December 2020 at University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India. The book encompasses all branches of artificial intelligence, computational sciences and machine learning which is based on computation at some level such as AI-based Internet of things, sensor networks, robotics, intelligent diabetic retinopathy, intelligent cancer genes analysis using computer vision, evolutionary algorithms, fuzzy systems, medical automatic identification intelligence system and applications in agriculture, health care, smart grid and instrumentation systems. The book is helpful for educators, researchers and developers working in the area of recent advances and upcoming technologies utilizing computational sciences in signal processing, imaging, computing, instrumentation, artificial intelligence and their applications.


Fraud Detection of Credit Card by Using HMM Model

Fraud Detection of Credit Card by Using HMM Model
Author: Sadhana Yadav
Publisher:
Total Pages: 0
Release: 2018
Genre:
ISBN:

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Credit card fraud is a serious and growing problem. Due to a rapid advancement in the electronic commerce technology, the use of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In this paper, we model the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) and show how it can be used for the detection of frauds. An HMM is initially trained with the normal behavior of a cardholder. If an incoming credit card transaction is not accepted by the trained HMM with a sufficiently high probability, it is considered to be fraudulent. At the same time, we try to ensure that genuine transactions are not rejected. We present detailed experimental results to show the effectiveness of our approach and compare it with other techniques available in the literature.